Discovery of aggressive cancer cell types made possible with machine learning techniques

By applying unsupervised and automated machine learning techniques to the analysis of millions of cancer cells, Rebecca Ihrie and Jonathan Irish, both associate professors of cell and developmental biology, have identified new cancer cell types in brain tumors. Machine learning is a series of computer algorithms that can identify patterns within enormous quantities of data and get'smarter' with more experience. This finding holds the promise of enabling researchers to better understand and target these cell types for research and therapeutics for glioblastoma--an aggressive brain tumor with high mortality--as well as the broader applicability of machine learning to cancer research. With their collaborators, Ihrie and Irish developed Risk Assessment Population IDentification (RAPID), an open-source machine learning algorithm that revealed coordinated patterns of protein expression and modification associated with survival outcomes. The article, "Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells" was published online in the journal eLife on June 23.